Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations25558
Missing cells53929
Missing cells (%)8.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory30.1 MiB
Average record size in memory1.2 KiB

Variable types

Numeric2
Categorical11
Text8
DateTime1
Boolean4

Alerts

Aircraft: Type has constant value "Airplane"Constant
Conditions: Precipitation is highly overall correlated with Number of people injuredHigh correlation
Effect: Impact to flight is highly overall correlated with When: Phase of flightHigh correlation
Number of people injured is highly overall correlated with Conditions: PrecipitationHigh correlation
When: Phase of flight is highly overall correlated with Effect: Impact to flightHigh correlation
Wildlife: Number Struck Actual is highly overall correlated with Wildlife: Number struckHigh correlation
Wildlife: Number struck is highly overall correlated with Wildlife: Number Struck ActualHigh correlation
Wildlife: Number struck is highly imbalanced (62.3%)Imbalance
Effect: Indicated Damage is highly imbalanced (54.1%)Imbalance
Aircraft: Number of engines? is highly imbalanced (75.9%)Imbalance
Conditions: Precipitation is highly imbalanced (52.8%)Imbalance
Remains of wildlife sent to Smithsonian is highly imbalanced (61.0%)Imbalance
Number of people injured is highly imbalanced (99.5%)Imbalance
Effect: Impact to flight has 23480 (91.9%) missing valuesMissing
Aircraft: Number of engines? has 267 (1.0%) missing valuesMissing
Origin State has 449 (1.8%) missing valuesMissing
Conditions: Precipitation has 23543 (92.1%) missing valuesMissing
Remarks has 4771 (18.7%) missing valuesMissing
Wildlife: Number Struck Actual is highly skewed (γ1 = 43.80299472)Skewed
Record ID has unique valuesUnique

Reproduction

Analysis started2024-10-16 13:38:52.905718
Analysis finished2024-10-16 13:39:12.193612
Duration19.29 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

Record ID
Real number (ℝ)

UNIQUE 

Distinct25558
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean253916.09
Minimum1195
Maximum321909
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size199.8 KiB
2024-10-16T19:09:12.384422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1195
5-th percentile204970.7
Q1225783.75
median248749
Q3269168.75
95-th percentile315640.3
Maximum321909
Range320714
Interquartile range (IQR)43385

Descriptive statistics

Standard deviation38510.453
Coefficient of variation (CV)0.15166606
Kurtosis4.5074897
Mean253916.09
Median Absolute Deviation (MAD)21970.5
Skewness-0.52243912
Sum6.4895873 × 109
Variance1.483055 × 109
MonotonicityNot monotonic
2024-10-16T19:09:12.650429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202152 1
 
< 0.1%
254643 1
 
< 0.1%
258806 1
 
< 0.1%
257552 1
 
< 0.1%
257811 1
 
< 0.1%
258313 1
 
< 0.1%
258209 1
 
< 0.1%
254678 1
 
< 0.1%
257472 1
 
< 0.1%
257553 1
 
< 0.1%
Other values (25548) 25548
> 99.9%
ValueCountFrequency (%)
1195 1
< 0.1%
3019 1
< 0.1%
3500 1
< 0.1%
3504 1
< 0.1%
3597 1
< 0.1%
4064 1
< 0.1%
4074 1
< 0.1%
4076 1
< 0.1%
4090 1
< 0.1%
4091 1
< 0.1%
ValueCountFrequency (%)
321909 1
< 0.1%
321291 1
< 0.1%
321172 1
< 0.1%
321159 1
< 0.1%
321151 1
< 0.1%
320702 1
< 0.1%
320701 1
< 0.1%
320296 1
< 0.1%
320200 1
< 0.1%
320199 1
< 0.1%

Aircraft: Type
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing129
Missing (%)0.5%
Memory size1.4 MiB
Airplane
25429 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters203432
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAirplane
2nd rowAirplane
3rd rowAirplane
4th rowAirplane
5th rowAirplane

Common Values

ValueCountFrequency (%)
Airplane 25429
99.5%
(Missing) 129
 
0.5%

Length

2024-10-16T19:09:12.858021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T19:09:13.020205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
airplane 25429
100.0%

Most occurring characters

ValueCountFrequency (%)
A 25429
12.5%
i 25429
12.5%
r 25429
12.5%
p 25429
12.5%
l 25429
12.5%
a 25429
12.5%
n 25429
12.5%
e 25429
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 203432
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 25429
12.5%
i 25429
12.5%
r 25429
12.5%
p 25429
12.5%
l 25429
12.5%
a 25429
12.5%
n 25429
12.5%
e 25429
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 203432
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 25429
12.5%
i 25429
12.5%
r 25429
12.5%
p 25429
12.5%
l 25429
12.5%
a 25429
12.5%
n 25429
12.5%
e 25429
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 203432
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 25429
12.5%
i 25429
12.5%
r 25429
12.5%
p 25429
12.5%
l 25429
12.5%
a 25429
12.5%
n 25429
12.5%
e 25429
12.5%
Distinct1109
Distinct (%)4.4%
Missing129
Missing (%)0.5%
Memory size1.7 MiB
2024-10-16T19:09:13.476614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length45
Median length33
Mean length19.514452
Min length4

Characters and Unicode

Total characters496233
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique402 ?
Unique (%)1.6%

Sample

1st rowLAGUARDIA NY
2nd rowDALLAS/FORT WORTH INTL ARPT
3rd rowLAKEFRONT AIRPORT
4th rowSEATTLE-TACOMA INTL
5th rowNORFOLK INTL
ValueCountFrequency (%)
intl 16536
 
22.3%
arpt 8356
 
11.2%
city 1213
 
1.6%
airport 982
 
1.3%
regional 946
 
1.3%
county 926
 
1.2%
worth 820
 
1.1%
field 810
 
1.1%
san 803
 
1.1%
dallas/fort 803
 
1.1%
Other values (1402) 42113
56.7%
2024-10-16T19:09:14.056061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
48884
9.9%
T 48523
9.8%
A 45873
 
9.2%
L 43487
 
8.8%
N 42773
 
8.6%
I 39767
 
8.0%
R 34129
 
6.9%
E 28790
 
5.8%
O 28629
 
5.8%
S 18003
 
3.6%
Other values (25) 117375
23.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 496233
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
48884
9.9%
T 48523
9.8%
A 45873
 
9.2%
L 43487
 
8.8%
N 42773
 
8.6%
I 39767
 
8.0%
R 34129
 
6.9%
E 28790
 
5.8%
O 28629
 
5.8%
S 18003
 
3.6%
Other values (25) 117375
23.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 496233
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
48884
9.9%
T 48523
9.8%
A 45873
 
9.2%
L 43487
 
8.8%
N 42773
 
8.6%
I 39767
 
8.0%
R 34129
 
6.9%
E 28790
 
5.8%
O 28629
 
5.8%
S 18003
 
3.6%
Other values (25) 117375
23.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 496233
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
48884
9.9%
T 48523
9.8%
A 45873
 
9.2%
L 43487
 
8.8%
N 42773
 
8.6%
I 39767
 
8.0%
R 34129
 
6.9%
E 28790
 
5.8%
O 28629
 
5.8%
S 18003
 
3.6%
Other values (25) 117375
23.7%

Altitude bin
Categorical

Distinct2
Distinct (%)< 0.1%
Missing129
Missing (%)0.5%
Memory size1.4 MiB
< 1000 ft
20556 
> 1000 ft
4873 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters228861
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row> 1000 ft
2nd row< 1000 ft
3rd row< 1000 ft
4th row< 1000 ft
5th row< 1000 ft

Common Values

ValueCountFrequency (%)
< 1000 ft 20556
80.4%
> 1000 ft 4873
 
19.1%
(Missing) 129
 
0.5%

Length

2024-10-16T19:09:14.268961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T19:09:14.427992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
25429
33.3%
1000 25429
33.3%
ft 25429
33.3%

Most occurring characters

ValueCountFrequency (%)
0 76287
33.3%
50858
22.2%
1 25429
 
11.1%
f 25429
 
11.1%
t 25429
 
11.1%
< 20556
 
9.0%
> 4873
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 228861
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 76287
33.3%
50858
22.2%
1 25429
 
11.1%
f 25429
 
11.1%
t 25429
 
11.1%
< 20556
 
9.0%
> 4873
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 228861
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 76287
33.3%
50858
22.2%
1 25429
 
11.1%
f 25429
 
11.1%
t 25429
 
11.1%
< 20556
 
9.0%
> 4873
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 228861
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 76287
33.3%
50858
22.2%
1 25429
 
11.1%
f 25429
 
11.1%
t 25429
 
11.1%
< 20556
 
9.0%
> 4873
 
2.1%
Distinct351
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2024-10-16T19:09:15.020288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length19
Mean length8.1845606
Min length3

Characters and Unicode

Total characters209181
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique89 ?
Unique (%)0.3%

Sample

1st rowB-737-400
2nd rowMD-80
3rd rowC-500
4th rowB-737-400
5th rowCL-RJ100/200
ValueCountFrequency (%)
b-737-700 2488
 
8.5%
b-737-300 2309
 
7.9%
cl-rj100/200 1951
 
6.7%
a-320 1193
 
4.1%
a-319 1000
 
3.4%
b-757-200 992
 
3.4%
emb-145 990
 
3.4%
b-737-800 678
 
2.3%
md-82 638
 
2.2%
b-717-200 577
 
2.0%
Other values (440) 16348
56.1%
2024-10-16T19:09:15.886791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 35352
16.9%
- 33454
16.0%
7 23023
11.0%
3 14503
 
6.9%
B 13290
 
6.4%
2 8931
 
4.3%
1 8032
 
3.8%
A 6933
 
3.3%
C 6140
 
2.9%
E 5092
 
2.4%
Other values (28) 54431
26.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 209181
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 35352
16.9%
- 33454
16.0%
7 23023
11.0%
3 14503
 
6.9%
B 13290
 
6.4%
2 8931
 
4.3%
1 8032
 
3.8%
A 6933
 
3.3%
C 6140
 
2.9%
E 5092
 
2.4%
Other values (28) 54431
26.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 209181
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 35352
16.9%
- 33454
16.0%
7 23023
11.0%
3 14503
 
6.9%
B 13290
 
6.4%
2 8931
 
4.3%
1 8032
 
3.8%
A 6933
 
3.3%
C 6140
 
2.9%
E 5092
 
2.4%
Other values (28) 54431
26.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 209181
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 35352
16.9%
- 33454
16.0%
7 23023
11.0%
3 14503
 
6.9%
B 13290
 
6.4%
2 8931
 
4.3%
1 8032
 
3.8%
A 6933
 
3.3%
C 6140
 
2.9%
E 5092
 
2.4%
Other values (28) 54431
26.0%

Wildlife: Number struck
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing129
Missing (%)0.5%
Memory size1.2 MiB
1
20790 
2 to 10
4319 
11 to 100
 
312
Over 100
 
8

Length

Max length9
Median length1
Mean length2.1194306
Min length1

Characters and Unicode

Total characters53895
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOver 100
2nd rowOver 100
3rd rowOver 100
4th rowOver 100
5th rowOver 100

Common Values

ValueCountFrequency (%)
1 20790
81.3%
2 to 10 4319
 
16.9%
11 to 100 312
 
1.2%
Over 100 8
 
< 0.1%
(Missing) 129
 
0.5%

Length

2024-10-16T19:09:16.160615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T19:09:16.377106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 20790
59.9%
to 4631
 
13.3%
2 4319
 
12.4%
10 4319
 
12.4%
100 320
 
0.9%
11 312
 
0.9%
over 8
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 26053
48.3%
9270
 
17.2%
0 4959
 
9.2%
t 4631
 
8.6%
o 4631
 
8.6%
2 4319
 
8.0%
O 8
 
< 0.1%
v 8
 
< 0.1%
e 8
 
< 0.1%
r 8
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 53895
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 26053
48.3%
9270
 
17.2%
0 4959
 
9.2%
t 4631
 
8.6%
o 4631
 
8.6%
2 4319
 
8.0%
O 8
 
< 0.1%
v 8
 
< 0.1%
e 8
 
< 0.1%
r 8
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 53895
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 26053
48.3%
9270
 
17.2%
0 4959
 
9.2%
t 4631
 
8.6%
o 4631
 
8.6%
2 4319
 
8.0%
O 8
 
< 0.1%
v 8
 
< 0.1%
e 8
 
< 0.1%
r 8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 53895
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 26053
48.3%
9270
 
17.2%
0 4959
 
9.2%
t 4631
 
8.6%
o 4631
 
8.6%
2 4319
 
8.0%
O 8
 
< 0.1%
v 8
 
< 0.1%
e 8
 
< 0.1%
r 8
 
< 0.1%

Wildlife: Number Struck Actual
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct106
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6915252
Minimum1
Maximum942
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size199.8 KiB
2024-10-16T19:09:16.609383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile8
Maximum942
Range941
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12.793975
Coefficient of variation (CV)4.7534295
Kurtosis2727.0892
Mean2.6915252
Median Absolute Deviation (MAD)0
Skewness43.802995
Sum68790
Variance163.6858
MonotonicityNot monotonic
2024-10-16T19:09:17.066270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 20915
81.8%
5 536
 
2.1%
7 496
 
1.9%
9 487
 
1.9%
4 484
 
1.9%
3 470
 
1.8%
2 468
 
1.8%
6 463
 
1.8%
10 461
 
1.8%
8 458
 
1.8%
Other values (96) 320
 
1.3%
ValueCountFrequency (%)
1 20915
81.8%
2 468
 
1.8%
3 470
 
1.8%
4 484
 
1.9%
5 536
 
2.1%
6 463
 
1.8%
7 496
 
1.9%
8 458
 
1.8%
9 487
 
1.9%
10 461
 
1.8%
ValueCountFrequency (%)
942 1
 
< 0.1%
859 1
 
< 0.1%
806 1
 
< 0.1%
537 1
 
< 0.1%
424 1
 
< 0.1%
320 1
 
< 0.1%
261 1
 
< 0.1%
227 1
 
< 0.1%
100 1
 
< 0.1%
99 4
< 0.1%

Effect: Impact to flight
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)0.2%
Missing23480
Missing (%)91.9%
Memory size1.4 MiB
Precautionary Landing
1121 
Aborted Take-off
479 
Other
390 
Engine Shut Down
 
88

Length

Max length21
Median length21
Mean length16.63282
Min length5

Characters and Unicode

Total characters34563
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEngine Shut Down
2nd rowPrecautionary Landing
3rd rowOther
4th rowOther
5th rowAborted Take-off

Common Values

ValueCountFrequency (%)
Precautionary Landing 1121
 
4.4%
Aborted Take-off 479
 
1.9%
Other 390
 
1.5%
Engine Shut Down 88
 
0.3%
(Missing) 23480
91.9%

Length

2024-10-16T19:09:17.327082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T19:09:17.545372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
precautionary 1121
29.1%
landing 1121
29.1%
aborted 479
12.4%
take-off 479
12.4%
other 390
 
10.1%
engine 88
 
2.3%
shut 88
 
2.3%
down 88
 
2.3%

Most occurring characters

ValueCountFrequency (%)
a 3842
 
11.1%
n 3627
 
10.5%
r 3111
 
9.0%
e 2557
 
7.4%
i 2330
 
6.7%
o 2167
 
6.3%
t 2078
 
6.0%
1776
 
5.1%
d 1600
 
4.6%
g 1209
 
3.5%
Other values (17) 10266
29.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34563
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3842
 
11.1%
n 3627
 
10.5%
r 3111
 
9.0%
e 2557
 
7.4%
i 2330
 
6.7%
o 2167
 
6.3%
t 2078
 
6.0%
1776
 
5.1%
d 1600
 
4.6%
g 1209
 
3.5%
Other values (17) 10266
29.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34563
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3842
 
11.1%
n 3627
 
10.5%
r 3111
 
9.0%
e 2557
 
7.4%
i 2330
 
6.7%
o 2167
 
6.3%
t 2078
 
6.0%
1776
 
5.1%
d 1600
 
4.6%
g 1209
 
3.5%
Other values (17) 10266
29.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34563
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3842
 
11.1%
n 3627
 
10.5%
r 3111
 
9.0%
e 2557
 
7.4%
i 2330
 
6.7%
o 2167
 
6.3%
t 2078
 
6.0%
1776
 
5.1%
d 1600
 
4.6%
g 1209
 
3.5%
Other values (17) 10266
29.7%
Distinct4225
Distinct (%)16.6%
Missing129
Missing (%)0.5%
Memory size199.8 KiB
Minimum2000-01-02 00:00:00
Maximum2011-12-31 00:00:00
2024-10-16T19:09:17.825735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:09:18.083986image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Effect: Indicated Damage
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
No damage
23081 
Caused damage
2477 

Length

Max length13
Median length9
Mean length9.3876673
Min length9

Characters and Unicode

Total characters239930
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCaused damage
2nd rowCaused damage
3rd rowNo damage
4th rowNo damage
5th rowNo damage

Common Values

ValueCountFrequency (%)
No damage 23081
90.3%
Caused damage 2477
 
9.7%

Length

2024-10-16T19:09:18.352599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T19:09:18.549342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
damage 25558
50.0%
no 23081
45.2%
caused 2477
 
4.8%

Most occurring characters

ValueCountFrequency (%)
a 53593
22.3%
d 28035
11.7%
e 28035
11.7%
25558
10.7%
m 25558
10.7%
g 25558
10.7%
N 23081
9.6%
o 23081
9.6%
C 2477
 
1.0%
u 2477
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 239930
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 53593
22.3%
d 28035
11.7%
e 28035
11.7%
25558
10.7%
m 25558
10.7%
g 25558
10.7%
N 23081
9.6%
o 23081
9.6%
C 2477
 
1.0%
u 2477
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 239930
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 53593
22.3%
d 28035
11.7%
e 28035
11.7%
25558
10.7%
m 25558
10.7%
g 25558
10.7%
N 23081
9.6%
o 23081
9.6%
C 2477
 
1.0%
u 2477
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 239930
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 53593
22.3%
d 28035
11.7%
e 28035
11.7%
25558
10.7%
m 25558
10.7%
g 25558
10.7%
N 23081
9.6%
o 23081
9.6%
C 2477
 
1.0%
u 2477
 
1.0%

Aircraft: Number of engines?
Categorical

IMBALANCE  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing267
Missing (%)1.0%
Memory size1.2 MiB
2
23025 
1
 
1313
3
 
564
4
 
388
C
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25291
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 23025
90.1%
1 1313
 
5.1%
3 564
 
2.2%
4 388
 
1.5%
C 1
 
< 0.1%
(Missing) 267
 
1.0%

Length

2024-10-16T19:09:18.740525image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T19:09:18.941832image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 23025
91.0%
1 1313
 
5.2%
3 564
 
2.2%
4 388
 
1.5%
c 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
2 23025
91.0%
1 1313
 
5.2%
3 564
 
2.2%
4 388
 
1.5%
C 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25291
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 23025
91.0%
1 1313
 
5.2%
3 564
 
2.2%
4 388
 
1.5%
C 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25291
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 23025
91.0%
1 1313
 
5.2%
3 564
 
2.2%
4 388
 
1.5%
C 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25291
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 23025
91.0%
1 1313
 
5.2%
3 564
 
2.2%
4 388
 
1.5%
C 1
 
< 0.1%
Distinct292
Distinct (%)1.1%
Missing129
Missing (%)0.5%
Memory size1.6 MiB
2024-10-16T19:09:19.504798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length33
Median length30
Mean length15.076645
Min length3

Characters and Unicode

Total characters383384
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81 ?
Unique (%)0.3%

Sample

1st rowUS AIRWAYS*
2nd rowAMERICAN AIRLINES
3rd rowBUSINESS
4th rowALASKA AIRLINES
5th rowCOMAIR AIRLINES
ValueCountFrequency (%)
airlines 13991
27.6%
southwest 4636
 
9.1%
business 3103
 
6.1%
air 3046
 
6.0%
american 3010
 
5.9%
airways 2591
 
5.1%
us 1353
 
2.7%
lines 1351
 
2.7%
delta 1350
 
2.7%
eagle 932
 
1.8%
Other values (315) 15403
30.3%
2024-10-16T19:09:20.430117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I 48786
12.7%
S 46497
12.1%
A 42174
11.0%
E 41363
10.8%
N 30252
7.9%
R 28965
7.6%
25338
 
6.6%
L 22203
 
5.8%
T 21513
 
5.6%
U 12446
 
3.2%
Other values (25) 63847
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 383384
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 48786
12.7%
S 46497
12.1%
A 42174
11.0%
E 41363
10.8%
N 30252
7.9%
R 28965
7.6%
25338
 
6.6%
L 22203
 
5.8%
T 21513
 
5.6%
U 12446
 
3.2%
Other values (25) 63847
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 383384
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 48786
12.7%
S 46497
12.1%
A 42174
11.0%
E 41363
10.8%
N 30252
7.9%
R 28965
7.6%
25338
 
6.6%
L 22203
 
5.8%
T 21513
 
5.6%
U 12446
 
3.2%
Other values (25) 63847
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 383384
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 48786
12.7%
S 46497
12.1%
A 42174
11.0%
E 41363
10.8%
N 30252
7.9%
R 28965
7.6%
25338
 
6.6%
L 22203
 
5.8%
T 21513
 
5.6%
U 12446
 
3.2%
Other values (25) 63847
16.7%

Origin State
Text

MISSING 

Distinct60
Distinct (%)0.2%
Missing449
Missing (%)1.8%
Memory size1.4 MiB
2024-10-16T19:09:20.858952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length25
Median length16
Mean length8.0386714
Min length2

Characters and Unicode

Total characters201843
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNew York
2nd rowTexas
3rd rowLouisiana
4th rowWashington
5th rowVirginia
ValueCountFrequency (%)
california 2520
 
8.8%
texas 2453
 
8.5%
new 2133
 
7.4%
florida 2055
 
7.2%
york 1319
 
4.6%
illinois 1008
 
3.5%
pennsylvania 986
 
3.4%
missouri 960
 
3.3%
carolina 818
 
2.9%
kentucky 812
 
2.8%
Other values (58) 13634
47.5%
2024-10-16T19:09:21.431782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 25907
12.8%
i 22108
 
11.0%
o 16444
 
8.1%
n 15977
 
7.9%
e 13323
 
6.6%
s 13292
 
6.6%
r 12721
 
6.3%
l 10474
 
5.2%
C 4692
 
2.3%
t 4566
 
2.3%
Other values (39) 62339
30.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 201843
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 25907
12.8%
i 22108
 
11.0%
o 16444
 
8.1%
n 15977
 
7.9%
e 13323
 
6.6%
s 13292
 
6.6%
r 12721
 
6.3%
l 10474
 
5.2%
C 4692
 
2.3%
t 4566
 
2.3%
Other values (39) 62339
30.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 201843
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 25907
12.8%
i 22108
 
11.0%
o 16444
 
8.1%
n 15977
 
7.9%
e 13323
 
6.6%
s 13292
 
6.6%
r 12721
 
6.3%
l 10474
 
5.2%
C 4692
 
2.3%
t 4566
 
2.3%
Other values (39) 62339
30.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 201843
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 25907
12.8%
i 22108
 
11.0%
o 16444
 
8.1%
n 15977
 
7.9%
e 13323
 
6.6%
s 13292
 
6.6%
r 12721
 
6.3%
l 10474
 
5.2%
C 4692
 
2.3%
t 4566
 
2.3%
Other values (39) 62339
30.9%

When: Phase of flight
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)< 0.1%
Missing129
Missing (%)0.5%
Memory size1.4 MiB
Approach
10382 
Landing Roll
5047 
Take-off run
4711 
Climb
4429 
Descent
 
776
Other values (2)
 
84

Length

Max length12
Median length8
Mean length8.9694837
Min length4

Characters and Unicode

Total characters228085
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClimb
2nd rowLanding Roll
3rd rowApproach
4th rowClimb
5th rowApproach

Common Values

ValueCountFrequency (%)
Approach 10382
40.6%
Landing Roll 5047
19.7%
Take-off run 4711
18.4%
Climb 4429
17.3%
Descent 776
 
3.0%
Taxi 74
 
0.3%
Parked 10
 
< 0.1%
(Missing) 129
 
0.5%

Length

2024-10-16T19:09:21.642684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T19:09:21.864348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
approach 10382
29.5%
landing 5047
14.3%
roll 5047
14.3%
take-off 4711
13.4%
run 4711
13.4%
climb 4429
12.6%
descent 776
 
2.2%
taxi 74
 
0.2%
parked 10
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
p 20764
 
9.1%
a 20224
 
8.9%
o 20140
 
8.8%
n 15581
 
6.8%
r 15103
 
6.6%
l 14523
 
6.4%
c 11158
 
4.9%
A 10382
 
4.6%
h 10382
 
4.6%
9758
 
4.3%
Other values (19) 80070
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 228085
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 20764
 
9.1%
a 20224
 
8.9%
o 20140
 
8.8%
n 15581
 
6.8%
r 15103
 
6.6%
l 14523
 
6.4%
c 11158
 
4.9%
A 10382
 
4.6%
h 10382
 
4.6%
9758
 
4.3%
Other values (19) 80070
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 228085
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 20764
 
9.1%
a 20224
 
8.9%
o 20140
 
8.8%
n 15581
 
6.8%
r 15103
 
6.6%
l 14523
 
6.4%
c 11158
 
4.9%
A 10382
 
4.6%
h 10382
 
4.6%
9758
 
4.3%
Other values (19) 80070
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 228085
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 20764
 
9.1%
a 20224
 
8.9%
o 20140
 
8.8%
n 15581
 
6.8%
r 15103
 
6.6%
l 14523
 
6.4%
c 11158
 
4.9%
A 10382
 
4.6%
h 10382
 
4.6%
9758
 
4.3%
Other values (19) 80070
35.1%

Conditions: Precipitation
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct7
Distinct (%)0.3%
Missing23543
Missing (%)92.1%
Memory size1.4 MiB
Rain
1353 
Fog
475 
Snow
 
89
Fog, Rain
 
85
Rain, Snow
 
6
Other values (2)
 
7

Length

Max length15
Median length4
Mean length4.0193548
Min length3

Characters and Unicode

Total characters8099
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSnow
2nd rowFog
3rd rowRain
4th rowRain
5th rowRain

Common Values

ValueCountFrequency (%)
Rain 1353
 
5.3%
Fog 475
 
1.9%
Snow 89
 
0.3%
Fog, Rain 85
 
0.3%
Rain, Snow 6
 
< 0.1%
Fog, Snow 4
 
< 0.1%
Fog, Rain, Snow 3
 
< 0.1%
(Missing) 23543
92.1%

Length

2024-10-16T19:09:22.138285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T19:09:22.348644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
rain 1447
68.4%
fog 567
 
26.8%
snow 102
 
4.8%

Most occurring characters

ValueCountFrequency (%)
n 1549
19.1%
R 1447
17.9%
a 1447
17.9%
i 1447
17.9%
o 669
8.3%
F 567
 
7.0%
g 567
 
7.0%
S 102
 
1.3%
w 102
 
1.3%
, 101
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8099
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1549
19.1%
R 1447
17.9%
a 1447
17.9%
i 1447
17.9%
o 669
8.3%
F 567
 
7.0%
g 567
 
7.0%
S 102
 
1.3%
w 102
 
1.3%
, 101
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8099
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1549
19.1%
R 1447
17.9%
a 1447
17.9%
i 1447
17.9%
o 669
8.3%
F 567
 
7.0%
g 567
 
7.0%
S 102
 
1.3%
w 102
 
1.3%
, 101
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8099
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1549
19.1%
R 1447
17.9%
a 1447
17.9%
i 1447
17.9%
o 669
8.3%
F 567
 
7.0%
g 567
 
7.0%
S 102
 
1.3%
w 102
 
1.3%
, 101
 
1.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 KiB
False
18732 
True
6826 
ValueCountFrequency (%)
False 18732
73.3%
True 6826
 
26.7%
2024-10-16T19:09:22.549456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 KiB
False
23601 
True
 
1957
ValueCountFrequency (%)
False 23601
92.3%
True 1957
 
7.7%
2024-10-16T19:09:22.674206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Remarks
Text

MISSING 

Distinct18186
Distinct (%)87.5%
Missing4771
Missing (%)18.7%
Memory size2.8 MiB
2024-10-16T19:09:23.154883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length255
Median length205
Mean length86.522779
Min length1

Characters and Unicode

Total characters1798549
Distinct characters86
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17886 ?
Unique (%)86.0%

Sample

1st rowFLT 753. PILOT REPTD A HUNDRED BIRDS ON UNKN TYPE. #1 ENG WAS SHUT DOWN AND DIVERTED TO EWR. SLIGHT VIBRATION. A/C WAS OUT OF SVC FOR REPAIRS TO COWLING, FAN DUCT ACCOUSTIC PANEL. INGESTION. DENTED FAN BLADE #26 IN #1 ENG. HEAVY BLOOD STAINS ON L WINGTIP
2nd row102 CARCASSES FOUND. 1 LDG LIGHT ON NOSE GEAR WAS DAMAGED AND REPLACED.
3rd rowFLEW UNDER A VERY LARGE FLOCK OF BIRDS OVER APCH END OF RWY. NO DMG. JUST A LOT OF BIRD DROPPINGS ON WINDSCREEN.
4th rowNOTAM WARNING. 26 BIRDS HIT THE A/C, FORCING AN EMERGENCY LDG. 77 BIRDS WERE FOUND DEAD ON RWY/TWY WITH GRASSHOPPERS IN THEIR STOMACHS. SAFETY AREAS COULD NOT BE THOROUGHLY INSPCTD DURING 14 MINUTE SHUTDOWN OF RWY 34L. NO DMG. A/C OUT OF SVC 40 MINS. PHOT
5th rowNO DMG REPTD.
ValueCountFrequency (%)
no 11811
 
3.5%
dmg 10281
 
3.0%
on 8917
 
2.6%
of 8133
 
2.4%
bird 7361
 
2.2%
to 6611
 
1.9%
reptd 6522
 
1.9%
and 5720
 
1.7%
rwy 4554
 
1.3%
a/c 4409
 
1.3%
Other values (12676) 265277
78.1%
2024-10-16T19:09:24.005979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
320421
17.8%
E 124138
 
6.9%
T 112531
 
6.3%
O 111480
 
6.2%
N 110901
 
6.2%
A 102182
 
5.7%
R 99529
 
5.5%
I 96782
 
5.4%
D 93897
 
5.2%
S 83378
 
4.6%
Other values (76) 543310
30.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1798549
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
320421
17.8%
E 124138
 
6.9%
T 112531
 
6.3%
O 111480
 
6.2%
N 110901
 
6.2%
A 102182
 
5.7%
R 99529
 
5.5%
I 96782
 
5.4%
D 93897
 
5.2%
S 83378
 
4.6%
Other values (76) 543310
30.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1798549
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
320421
17.8%
E 124138
 
6.9%
T 112531
 
6.3%
O 111480
 
6.2%
N 110901
 
6.2%
A 102182
 
5.7%
R 99529
 
5.5%
I 96782
 
5.4%
D 93897
 
5.2%
S 83378
 
4.6%
Other values (76) 543310
30.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1798549
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
320421
17.8%
E 124138
 
6.9%
T 112531
 
6.3%
O 111480
 
6.2%
N 110901
 
6.2%
A 102182
 
5.7%
R 99529
 
5.5%
I 96782
 
5.4%
D 93897
 
5.2%
S 83378
 
4.6%
Other values (76) 543310
30.2%

Wildlife: Size
Categorical

Distinct3
Distinct (%)< 0.1%
Missing129
Missing (%)0.5%
Memory size1.3 MiB
Small
17412 
Medium
5937 
Large
2080 

Length

Max length6
Median length5
Mean length5.2334736
Min length5

Characters and Unicode

Total characters133082
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium
2nd rowSmall
3rd rowSmall
4th rowSmall
5th rowSmall

Common Values

ValueCountFrequency (%)
Small 17412
68.1%
Medium 5937
 
23.2%
Large 2080
 
8.1%
(Missing) 129
 
0.5%

Length

2024-10-16T19:09:24.247462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T19:09:24.423413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
small 17412
68.5%
medium 5937
 
23.3%
large 2080
 
8.2%

Most occurring characters

ValueCountFrequency (%)
l 34824
26.2%
m 23349
17.5%
a 19492
14.6%
S 17412
13.1%
e 8017
 
6.0%
M 5937
 
4.5%
d 5937
 
4.5%
i 5937
 
4.5%
u 5937
 
4.5%
L 2080
 
1.6%
Other values (2) 4160
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 133082
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 34824
26.2%
m 23349
17.5%
a 19492
14.6%
S 17412
13.1%
e 8017
 
6.0%
M 5937
 
4.5%
d 5937
 
4.5%
i 5937
 
4.5%
u 5937
 
4.5%
L 2080
 
1.6%
Other values (2) 4160
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 133082
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 34824
26.2%
m 23349
17.5%
a 19492
14.6%
S 17412
13.1%
e 8017
 
6.0%
M 5937
 
4.5%
d 5937
 
4.5%
i 5937
 
4.5%
u 5937
 
4.5%
L 2080
 
1.6%
Other values (2) 4160
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 133082
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 34824
26.2%
m 23349
17.5%
a 19492
14.6%
S 17412
13.1%
e 8017
 
6.0%
M 5937
 
4.5%
d 5937
 
4.5%
i 5937
 
4.5%
u 5937
 
4.5%
L 2080
 
1.6%
Other values (2) 4160
 
3.1%

Conditions: Sky
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
No Cloud
12642 
Some Cloud
8706 
Overcast
4210 

Length

Max length10
Median length8
Mean length8.681274
Min length8

Characters and Unicode

Total characters221876
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Cloud
2nd rowSome Cloud
3rd rowNo Cloud
4th rowSome Cloud
5th rowNo Cloud

Common Values

ValueCountFrequency (%)
No Cloud 12642
49.5%
Some Cloud 8706
34.1%
Overcast 4210
 
16.5%

Length

2024-10-16T19:09:24.652444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T19:09:24.846985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
cloud 21348
45.5%
no 12642
27.0%
some 8706
18.6%
overcast 4210
 
9.0%

Most occurring characters

ValueCountFrequency (%)
o 42696
19.2%
21348
9.6%
C 21348
9.6%
l 21348
9.6%
u 21348
9.6%
d 21348
9.6%
e 12916
 
5.8%
N 12642
 
5.7%
m 8706
 
3.9%
S 8706
 
3.9%
Other values (7) 29470
13.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 221876
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 42696
19.2%
21348
9.6%
C 21348
9.6%
l 21348
9.6%
u 21348
9.6%
d 21348
9.6%
e 12916
 
5.8%
N 12642
 
5.7%
m 8706
 
3.9%
S 8706
 
3.9%
Other values (7) 29470
13.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 221876
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 42696
19.2%
21348
9.6%
C 21348
9.6%
l 21348
9.6%
u 21348
9.6%
d 21348
9.6%
e 12916
 
5.8%
N 12642
 
5.7%
m 8706
 
3.9%
S 8706
 
3.9%
Other values (7) 29470
13.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 221876
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 42696
19.2%
21348
9.6%
C 21348
9.6%
l 21348
9.6%
u 21348
9.6%
d 21348
9.6%
e 12916
 
5.8%
N 12642
 
5.7%
m 8706
 
3.9%
S 8706
 
3.9%
Other values (7) 29470
13.3%
Distinct348
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
2024-10-16T19:09:25.375977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length29
Median length28
Mean length17.635457
Min length4

Characters and Unicode

Total characters450727
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique101 ?
Unique (%)0.4%

Sample

1st rowUnknown bird - medium
2nd rowRock pigeon
3rd rowEuropean starling
4th rowEuropean starling
5th rowEuropean starling
ValueCountFrequency (%)
unknown 15680
19.2%
bird 15680
19.2%
15620
19.2%
small 10505
12.9%
medium 4318
 
5.3%
dove 995
 
1.2%
swallow 967
 
1.2%
mourning 898
 
1.1%
european 889
 
1.1%
starling 885
 
1.1%
Other values (378) 15025
18.4%
2024-10-16T19:09:26.197307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 56724
12.6%
55904
12.4%
l 32026
 
7.1%
i 27398
 
6.1%
r 27203
 
6.0%
d 25406
 
5.6%
o 25063
 
5.6%
a 21944
 
4.9%
m 20635
 
4.6%
w 19669
 
4.4%
Other values (45) 138755
30.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 450727
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 56724
12.6%
55904
12.4%
l 32026
 
7.1%
i 27398
 
6.1%
r 27203
 
6.0%
d 25406
 
5.6%
o 25063
 
5.6%
a 21944
 
4.9%
m 20635
 
4.6%
w 19669
 
4.4%
Other values (45) 138755
30.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 450727
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 56724
12.6%
55904
12.4%
l 32026
 
7.1%
i 27398
 
6.1%
r 27203
 
6.0%
d 25406
 
5.6%
o 25063
 
5.6%
a 21944
 
4.9%
m 20635
 
4.6%
w 19669
 
4.4%
Other values (45) 138755
30.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 450727
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 56724
12.6%
55904
12.4%
l 32026
 
7.1%
i 27398
 
6.1%
r 27203
 
6.0%
d 25406
 
5.6%
o 25063
 
5.6%
a 21944
 
4.9%
m 20635
 
4.6%
w 19669
 
4.4%
Other values (45) 138755
30.8%
Distinct2
Distinct (%)< 0.1%
Missing129
Missing (%)0.5%
Memory size50.0 KiB
False
14567 
True
10862 
(Missing)
 
129
ValueCountFrequency (%)
False 14567
57.0%
True 10862
42.5%
(Missing) 129
 
0.5%
2024-10-16T19:09:26.404037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Distinct803
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2024-10-16T19:09:27.015547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length11
Median length1
Mean length1.1958682
Min length1

Characters and Unicode

Total characters30564
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique617 ?
Unique (%)2.4%

Sample

1st row30,736
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 24330
95.2%
200 17
 
0.1%
53 17
 
0.1%
205 11
 
< 0.1%
308 10
 
< 0.1%
325 9
 
< 0.1%
316 9
 
< 0.1%
103 9
 
< 0.1%
513 9
 
< 0.1%
500 8
 
< 0.1%
Other values (793) 1129
 
4.4%
2024-10-16T19:09:27.922018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 25083
82.1%
, 1025
 
3.4%
1 797
 
2.6%
2 650
 
2.1%
3 541
 
1.8%
5 518
 
1.7%
4 465
 
1.5%
6 449
 
1.5%
7 369
 
1.2%
8 366
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30564
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 25083
82.1%
, 1025
 
3.4%
1 797
 
2.6%
2 650
 
2.1%
3 541
 
1.8%
5 518
 
1.7%
4 465
 
1.5%
6 449
 
1.5%
7 369
 
1.2%
8 366
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30564
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 25083
82.1%
, 1025
 
3.4%
1 797
 
2.6%
2 650
 
2.1%
3 541
 
1.8%
5 518
 
1.7%
4 465
 
1.5%
6 449
 
1.5%
7 369
 
1.2%
8 366
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30564
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 25083
82.1%
, 1025
 
3.4%
1 797
 
2.6%
2 650
 
2.1%
3 541
 
1.8%
5 518
 
1.7%
4 465
 
1.5%
6 449
 
1.5%
7 369
 
1.2%
8 366
 
1.2%
Distinct257
Distinct (%)1.0%
Missing129
Missing (%)0.5%
Memory size1.3 MiB
2024-10-16T19:09:28.436879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.5045814
Min length1

Characters and Unicode

Total characters63689
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique93 ?
Unique (%)0.4%

Sample

1st row1,500
2nd row0
3rd row50
4th row50
5th row50
ValueCountFrequency (%)
0 9843
38.7%
100 1357
 
5.3%
50 1259
 
5.0%
200 1001
 
3.9%
10 893
 
3.5%
500 890
 
3.5%
1,000 826
 
3.2%
300 683
 
2.7%
3,000 603
 
2.4%
2,000 578
 
2.3%
Other values (247) 7496
29.5%
2024-10-16T19:09:29.107156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 38674
60.7%
, 5699
 
8.9%
1 5133
 
8.1%
5 5026
 
7.9%
2 3381
 
5.3%
3 2094
 
3.3%
4 1298
 
2.0%
8 794
 
1.2%
7 692
 
1.1%
6 655
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63689
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 38674
60.7%
, 5699
 
8.9%
1 5133
 
8.1%
5 5026
 
7.9%
2 3381
 
5.3%
3 2094
 
3.3%
4 1298
 
2.0%
8 794
 
1.2%
7 692
 
1.1%
6 655
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63689
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 38674
60.7%
, 5699
 
8.9%
1 5133
 
8.1%
5 5026
 
7.9%
2 3381
 
5.3%
3 2094
 
3.3%
4 1298
 
2.0%
8 794
 
1.2%
7 692
 
1.1%
6 655
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63689
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 38674
60.7%
, 5699
 
8.9%
1 5133
 
8.1%
5 5026
 
7.9%
2 3381
 
5.3%
3 2094
 
3.3%
4 1298
 
2.0%
8 794
 
1.2%
7 692
 
1.1%
6 655
 
1.0%

Number of people injured
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0
25540 
1
 
13
2
 
4
6
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25558
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 25540
99.9%
1 13
 
0.1%
2 4
 
< 0.1%
6 1
 
< 0.1%

Length

2024-10-16T19:09:29.340687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T19:09:29.507860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 25540
99.9%
1 13
 
0.1%
2 4
 
< 0.1%
6 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 25540
99.9%
1 13
 
0.1%
2 4
 
< 0.1%
6 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25558
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 25540
99.9%
1 13
 
0.1%
2 4
 
< 0.1%
6 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25558
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 25540
99.9%
1 13
 
0.1%
2 4
 
< 0.1%
6 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25558
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 25540
99.9%
1 13
 
0.1%
2 4
 
< 0.1%
6 1
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing129
Missing (%)0.5%
Memory size50.0 KiB
False
17027 
True
8402 
(Missing)
 
129
ValueCountFrequency (%)
False 17027
66.6%
True 8402
32.9%
(Missing) 129
 
0.5%
2024-10-16T19:09:29.674236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Interactions

2024-10-16T19:09:09.546201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:09:08.968872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:09:09.721395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-16T19:09:09.354308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-16T19:09:29.980592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Aircraft: Number of engines?Altitude binConditions: PrecipitationConditions: SkyEffect: Impact to flightEffect: Indicated DamageIs Aircraft Large?Number of people injuredPilot warned of birds or wildlife?Record IDRemains of wildlife collected?Remains of wildlife sent to SmithsonianWhen: Phase of flightWildlife: Number Struck ActualWildlife: Number struckWildlife: Size
Aircraft: Number of engines?1.0000.0390.0500.0210.0890.1210.2020.0350.0640.0590.0500.0200.0420.0000.0200.076
Altitude bin0.0391.0000.0430.1290.2400.0950.1380.0210.1560.0210.1680.0130.4940.0000.1060.188
Conditions: Precipitation0.0500.0431.0000.1660.0000.0000.0001.0000.0950.0500.0830.0190.0480.0160.0280.009
Conditions: Sky0.0210.1290.1661.0000.0760.0150.0710.0000.1250.0230.1220.0260.1130.0020.0490.024
Effect: Impact to flight0.0890.2400.0000.0761.0000.2560.1300.0580.0680.0670.0760.1690.5350.0570.0460.113
Effect: Indicated Damage0.1210.0950.0000.0150.2561.0000.0580.0800.0330.0510.0500.1410.1310.0430.0870.393
Is Aircraft Large?0.2020.1380.0000.0710.1300.0581.0000.0120.0230.0730.0560.0000.1140.0000.0440.049
Number of people injured0.0350.0211.0000.0000.0580.0800.0121.0000.0080.0000.0000.0110.0090.0000.0310.032
Pilot warned of birds or wildlife?0.0640.1560.0950.1250.0680.0330.0230.0081.0000.1560.2570.1280.1910.0050.0630.039
Record ID0.0590.0210.0500.0230.0670.0510.0730.0000.1561.0000.2870.1830.033-0.0140.0240.075
Remains of wildlife collected?0.0500.1680.0830.1220.0760.0500.0560.0000.2570.2871.0000.4760.2480.0130.1090.081
Remains of wildlife sent to Smithsonian0.0200.0130.0190.0260.1690.1410.0000.0110.1280.1830.4761.0000.0240.0000.0570.022
When: Phase of flight0.0420.4940.0480.1130.5350.1310.1140.0090.1910.0330.2480.0241.0000.0000.0560.113
Wildlife: Number Struck Actual0.0000.0000.0160.0020.0570.0430.0000.0000.005-0.0140.0130.0000.0001.0000.5920.000
Wildlife: Number struck0.0200.1060.0280.0490.0460.0870.0440.0310.0630.0240.1090.0570.0560.5921.0000.056
Wildlife: Size0.0760.1880.0090.0240.1130.3930.0490.0320.0390.0750.0810.0220.1130.0000.0561.000

Missing values

2024-10-16T19:09:09.992960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-16T19:09:10.729117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-10-16T19:09:11.678670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Record IDAircraft: TypeAirport: NameAltitude binAircraft: Make/ModelWildlife: Number struckWildlife: Number Struck ActualEffect: Impact to flightFlightDateEffect: Indicated DamageAircraft: Number of engines?Aircraft: Airline/OperatorOrigin StateWhen: Phase of flightConditions: PrecipitationRemains of wildlife collected?Remains of wildlife sent to SmithsonianRemarksWildlife: SizeConditions: SkyWildlife: SpeciesPilot warned of birds or wildlife?Cost: Total $Feet above groundNumber of people injuredIs Aircraft Large?
0202152AirplaneLAGUARDIA NY> 1000 ftB-737-400Over 100859Engine Shut Down23-11-2000 00:00Caused damage2US AIRWAYS*New YorkClimbNaNFalseFalseFLT 753. PILOT REPTD A HUNDRED BIRDS ON UNKN TYPE. #1 ENG WAS SHUT DOWN AND DIVERTED TO EWR. SLIGHT VIBRATION. A/C WAS OUT OF SVC FOR REPAIRS TO COWLING, FAN DUCT ACCOUSTIC PANEL. INGESTION. DENTED FAN BLADE #26 IN #1 ENG. HEAVY BLOOD STAINS ON L WINGTIPMediumNo CloudUnknown bird - mediumN30,7361,5000Yes
1208159AirplaneDALLAS/FORT WORTH INTL ARPT< 1000 ftMD-80Over 100424NaN25-07-2001 00:00Caused damage2AMERICAN AIRLINESTexasLanding RollNaNFalseFalse102 CARCASSES FOUND. 1 LDG LIGHT ON NOSE GEAR WAS DAMAGED AND REPLACED.SmallSome CloudRock pigeonY000No
2207601AirplaneLAKEFRONT AIRPORT< 1000 ftC-500Over 100261NaN14-09-2001 00:00No damage2BUSINESSLouisianaApproachNaNFalseFalseFLEW UNDER A VERY LARGE FLOCK OF BIRDS OVER APCH END OF RWY. NO DMG. JUST A LOT OF BIRD DROPPINGS ON WINDSCREEN.SmallNo CloudEuropean starlingN0500No
3215953AirplaneSEATTLE-TACOMA INTL< 1000 ftB-737-400Over 100806Precautionary Landing05-09-2002 00:00No damage2ALASKA AIRLINESWashingtonClimbNaNTrueFalseNOTAM WARNING. 26 BIRDS HIT THE A/C, FORCING AN EMERGENCY LDG. 77 BIRDS WERE FOUND DEAD ON RWY/TWY WITH GRASSHOPPERS IN THEIR STOMACHS. SAFETY AREAS COULD NOT BE THOROUGHLY INSPCTD DURING 14 MINUTE SHUTDOWN OF RWY 34L. NO DMG. A/C OUT OF SVC 40 MINS. PHOTSmallSome CloudEuropean starlingY0500Yes
4219878AirplaneNORFOLK INTL< 1000 ftCL-RJ100/200Over 100942NaN23-06-2003 00:00No damage2COMAIR AIRLINESVirginiaApproachNaNFalseFalseNO DMG REPTD.SmallNo CloudEuropean starlingN0500No
5218432AirplaneGUAYAQUIL/S BOLIVAR< 1000 ftA-300Over 100537NaN24-07-2003 00:00No damage2AMERICAN AIRLINESNaNTake-off runNaNFalseFalseNO DMG. BIRD REMAINS ON F/O WINDSCREEN.SmallNo CloudUnknown bird - smallN000No
6221697AirplaneNEW CASTLE COUNTY< 1000 ftLEARJET-25Over 100227Other17-08-2003 00:00Caused damage2BUSINESSDelawareClimbNaNTrueTrueNaNSmallNo CloudEuropean starlingN14,81,7111500No
7236635AirplaneWASHINGTON DULLES INTL ARPT< 1000 ftA-320Over 100320Other01-03-2006 00:00Caused damage2UNITED AIRLINESDCApproachNaNTrueFalseWS ASSISTED IN CLEAN-UP OF 273 STARLINGS AND 1 BROWN-HEADED COWBIRD FROM RWY THRESHOLD. PHOTOS OF A/C TAKEN. BORESCOPED BOTH ENGS. FOUND DENTS AND NICKS IN STAGES 3-6. ALL WITHIN LIMITS. CLEANED RADOME, L WING, FLAPS, PYLON, GEAR AND LEADING EDGE FLAPS. RSmallSome CloudEuropean starlingY14,83,1411000No
8207369AirplaneATLANTA INTL< 1000 ftDC-9-302 to 109Aborted Take-off06-01-2000 00:00No damage2AIRTRAN AIRWAYSGeorgiaTake-off runNaNFalseFalseNaNSmallSome CloudRock pigeonN000No
9204371AirplaneORLANDO SANFORD INTL AIRPORT< 1000 ftA-3302 to 104NaN07-01-2000 00:00No damage2AIRTOURS INTLFloridaLanding RollNaNFalseFalseFLT 057SmallSome CloudUnknown bird - smallN000No
Record IDAircraft: TypeAirport: NameAltitude binAircraft: Make/ModelWildlife: Number struckWildlife: Number Struck ActualEffect: Impact to flightFlightDateEffect: Indicated DamageAircraft: Number of engines?Aircraft: Airline/OperatorOrigin StateWhen: Phase of flightConditions: PrecipitationRemains of wildlife collected?Remains of wildlife sent to SmithsonianRemarksWildlife: SizeConditions: SkyWildlife: SpeciesPilot warned of birds or wildlife?Cost: Total $Feet above groundNumber of people injuredIs Aircraft Large?
25548319663AirplaneGREATER ROCKFORD< 1000 ftA-30011NaN27-12-2011 00:00No damage2UPS AIRLINESIllinoisLanding RollNaNFalseFalsePILOT REPTD STRIKE TO BELOW THE WINDSCREEN ON F/O SIDE OF RADOME DURING FLARE. ARPT VEHICLE UNABLE TO FIND ANY REMAINS. NO CARCASS FOUND AFTER INSPN OF RWY FROM THRESHOLD TO 4000 RDR. UPS EVENT REPT 34473.MediumNo CloudUnknown bird - mediumN000No
25549319644AirplaneBELLINGHAM INTL< 1000 ftMD-8311NaN28-12-2011 00:00No damage2ALLEGIANT AIRWashingtonTake-off runNaNTrueFalseNaNSmallOvercastKilldeerN000No
25550319668AirplaneBALTIMORE WASH INTL< 1000 ftB-737-70011NaN28-12-2011 00:00No damage2SOUTHWEST AIRLINESMarylandLanding RollNaNTrueFalseNaNSmallNo CloudUnknown bird - smallY000Yes
25551319671AirplaneBELLINGHAM INTL< 1000 ftMD-8311NaN29-12-2011 00:00No damage2ALLEGIANT AIRWashingtonClimbNaNFalseFalseNO DMG TO A/C. POSSIBLY GULL?SmallSome CloudUnknown bird - smallN0500No
25552319672AirplaneSACRAMENTO INTL< 1000 ftB-737-70011NaN29-12-2011 00:00No damage2SOUTHWEST AIRLINESCaliforniaApproachNaNTrueTrueID BY SMITHSONIAN. SAMPLES FROM BOTH A/C AND BIRD BOTH = MALLARD. BIRD LODGED IN RT TRAINING EDGE FLAP. TOOK TWO MX ABOUT 20 MTS TO DO INSPN AND CLEAN UP REMAINS.MediumNo CloudMallardY0100Yes
25553321151AirplaneREDDING MUNICIPAL> 1000 ftEMB-12011NaN30-12-2011 00:00No damage2SKYWEST AIRLINESCaliforniaApproachFogFalseFalseDUCK? NO DMG REPTD.LargeOvercastUnknown bird - largeN01,5000No
25554319677AirplaneORLANDO INTL< 1000 ftA-32111NaN30-12-2011 00:00No damage2US AIRWAYSFloridaLanding RollNaNFalseFalseNaNSmallSome CloudTree swallowY000No
25555319680NaNNaNNaNEC-135NaN1NaNNaNNo damageNaNNaNVirginiaNaNNaNFalseFalseSTRUCK BIRD ON RT FRONT DURING T/O. BIRD REPTD AS BROWN/WHITE. TWY.NaNNo CloudUnknown bird - smallNaN0NaN0NaN
25556319679AirplaneDETROIT METRO WAYNE COUNTY ARPT< 1000 ftB-757-20011NaN31-12-2011 00:00No damage2DELTA AIR LINESMichiganLanding RollNaNFalseFalsePILOTS REPORT STRIKING UNKNOWN BIRD ON RWY 21L BTWN TWY F & J. NO REMAINS FOUND ON RWY OR ON A/C. NO DMG TO A/C.MediumSome CloudUnknown bird - mediumY000Yes
25557319593AirplaneABRAHAM LINCOLN CAPITAL ARPT< 1000 ftB-737-40011NaN31-12-2011 00:00Caused damage2XTRA AIRWAYSIllinoisTake-off runNaNTrueFalseHIT CENTER OF RADOME, CAVING IN ABOUT 12". RADOME WAS REPLACED. CARCASS FOUND IN SAFETY ARA ON RT SIDE OF RWY 22 AT INTXN OF RWY 18/36.MediumNo CloudRed-tailed hawkN000Yes